Behavioural
Brain
Research
259 (2014) 302–
312
Contents
lists
available
at
ScienceDirect
Behavioural
Brain
Research
j
our na
l
h
o
mepa
ge :
www. elsevi er. com/ loca te /bbr
Research
report
Interaction
between
serum
BDNF
and
aerobic
fitness
predicts
recognition
memory
in
healthy
young
adults
Andrew
S.
Whitemana,
Daniel
E.
Youngb,
Xuemei
Hec,
Tai
C.
Chenc,
Robert
C.
Wagenaard,
Chantal
E.
Sterna,
Karin
Schona,e,
Department
of
Psychology
and
Center
for
Memory
&
Brain,
Boston
University,
2
Cummington
Mall,
Boston,
MA
02215,
USA
bExercise
and
Health
Sciences
Department,
College
of
Nursing
and
Health
Sciences,
University
of
Massachusetts
Boston,
100
Morrissey
Blvd.,
Boston,
MA
02125,
USA
cDepartment
of
Medicine,
Section
of
Endocrinology,
Diabetes
and
Nutrition,
Boston
University
School
of
Medicine,
85
East
Newton
Street,
Boston,
MA
02118,
USA
dSargent
College
of
Health
and
Rehabilitation
Sciences,
Boston
University,
635
Commonwealth
Avenue,
Boston,
MA
02215,
USA
Department
of
Anatomy
and
Neurobiology,
Boston
University
School
of
Medicine,
650
Albany
Street,
Boston,
MA
02118,
USA
h
i
g
h
l
i
g
h
t
s
We
assessed
recognition
memory,
peripheral
neurotrophin
levels
and
aerobic
fitness.
On
its
own,
resting
serum
BDNF
is
negatively
associated
with
recognition
memory.
BDNF
and
aerobic
fitness
strongly
interact
to
positively
predict
recognition
memory.
This
interaction
may
support
an
exercise-related
change
in
BDNF
dose–response.
Resting
serum
IGF-1
is
positively
associated
with
aerobic
fitness.
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
22
July
2013
Received
in
revised
form
16
October
2013
Accepted
13
November
2013
Available online 21 November 2013
Keywords:
BDNF
IGF-1
Recognition
memory
Hippocampus
Cardiovascular
fitness
˙
VO
2
max
a
b
s
t
r
a
c
t
Convergent
evidence
from
human
and
non-human
animal
studies
suggests
aerobic
exercise
and
increased
aerobic
capacity
may
be
beneficial
for
brain
health
and
cognition.
It
is
thought
growth
factors
may
medi-
ate
this
putative
relationship,
particularly
by
augmenting
plasticity
mechanisms
in
the
hippocampus,
a
brain
region
critical
for
learning
and
memory.
Among
these
factors,
glucocorticoids,
brain
derived
neurotrophic
factor
(BDNF),
insulin-like
growth
factor-1
(IGF-1),
and
vascular
endothelial
growth
factor
(VEGF),
hormones
that
have
considerable
and
diverse
physiological
importance,
are
thought
to
effect
normal
and
exercise-induced
hippocampal
plasticity.
Despite
these
predictions,
relatively
few
published
human
studies
have
tested
hypotheses
that
relate
exercise
and
fitness
to
the
hippocampus,
and
none
have
considered
the
potential
links
to
all
of
these
hormonal
components.
Here
we
present
cross-sectional
data
from
a
study
of
recognition
memory;
serum
BDNF,
cortisol,
IGF-1,
and
VEGF
levels;
and
aerobic
capacity
in
healthy
young
adults.
We
measured
circulating
levels
of
these
hormones
together
with
performance
on
a
recognition
memory
task,
and
a
standard
graded
treadmill
test
of
aerobic
fitness.
Regression
anal-
yses
demonstrated
BDNF
and
aerobic
fitness
predict
recognition
memory
in
an
interactive
manner.
In
addition,
IGF-1
was
positively
associated
with
aerobic
fitness,
but
not
with
recognition
memory.
Our
results
may
suggest
an
exercise
adaptation-related
change
in
the
BDNF
dose–response
curve
that
relates
to
hippocampal
memory.
© 2013 Elsevier B.V. All rights reserved.
Abbreviations:
ACSM,
American
College
of
Sports
Medicine;
BDNF,
brain-derived
neurotrophic
factor;
BMI,
body
mass
index;
DMS,
delayed
matching-to-sample;
ELISA,
enzyme-linked
immunosorbent
assay;
IGF-1,
insulin-like
growth
factor-1;
MTL,
medial
temporal
lobes;
OLS,
ordinary
least
squares;
RER,
respiratory
exchange
ratio;
RERmax,
maximum
observed
respiratory
exchange
ratio;
SMT,
subsequent
memory
test;
VEGF,
vascular
endothelial
growth
factor;
˙
VO
2
max,
rate
of
maximal
oxygen
consumption
in
mL
per
kg
of
body
weight
per
min;
˙
VO
2
peak,
peak
rate
of
oxygen
consumption
in
mL
per
kg
of
body
weight
per
min,
measured
during
test.
Corresponding
author
at:
Department
of
Anatomy
and
Neurobiology,
Boston
University
School
of
Medicine,
650
Albany
Street,
X-141,
Boston,
MA
02118,
USA.
Tel.:
+1
617
414
2327;
fax:
+1
617
638
4216.
E-mail
address:
kschon@bu.edu
(K.
Schon).
0166-4328/$
see
front
matter ©
2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.bbr.2013.11.023
How to convert pdf to tiff image - SDK control API:C# PDF Convert to Tiff SDK: Convert PDF to tiff images in C#.net, ASP.NET MVC, Ajax, WinForms, WPF
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A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
303
1.
Introduction
Human
and
animal
studies
have
converged
on
the
idea
aerobic
exercise
and
cardio-respiratory
fitness
may
be
beneficial
for
brain
health
and
cognition.
In
animal
models,
rodents
that
run
consis-
tently
outperform
sedentary
controls
on
memory
tests
that
depend
on
the
hippocampus
(e.g.
[1,2]).
It
is
thought
aerobic
exercise
may
enhance
hippocampal
plasticity
and
memory
through
a
variety
of
hormonal
and
inflammatory
factors
(see
Ref.
[3]
for
a
review),
including
glucocorticoids,
and
neurotrophins—proteins
that
play
special
roles
in
the
growth
and
maintenance
of
the
nervous
and
car-
diovascular
systems
both
developmentally
and
in
adulthood
[4–6].
Neurotrophins
and
neurotrophin
related
genes
(e.g.
brain
derived
neurotrophic
factor;
BDNF)
are
upregulated
in
the
hippocampus
in
response
to
exercise
[7,8]
and
are
thought
to
be
critical
for
synaptic
plasticity
[9].
Although
rodent
studies
clearly
suggest
aerobic
exercise
may
preferentially
impact
the
hippocampal
memory
system,
human
studies
have
mostly
focused
on
executive
processes
(reviewed
in
Ref.
[10]).
In
humans,
links
between
physical
fitness
and
perfor-
mance
on
tasks
designed
to
probe
executive
functions
have
been
established
in
meta-analytic
studies
[11–14].
Recently,
exercise-
induced
changes
in
aerobic
capacity
were
associated
with
global
intelligence
measures
in
young
men
[15],
positively
correlated
with
delayed
free
recall
in
young
to
middle
aged
adults
[16]
and
with
pattern
separation
performance
in
young
adults
[17].
While
a
few
human
studies
have
also
focused
on
BDNF
specifically,
results
have
been
mixed.
Previous
work
has
suggested
changes
in
serum
BDNF
levels
following
chronic
exercise
are
associated
with
changes
in
hippocampal
volume
over
a
one
year
period
[18],
and
BDNF
may
predict
enhanced
performance
on
a
word-learning
task
following
an
acute
exercise
bout
[19].
Although
animal
models
predict
circulating
growth
factors
and
glucocorticoids
may
mediate
the
effects
of
aerobic
exercise
on
memory,
whether
the
presumed
relationship
between
aerobic
capacity
and
memory
enhancement
is
modulated
by
these
fac-
tors
in
humans
is
not
known.
To
address
this
question,
our
group
designed
a
cross
sectional
study
of
aerobic
capacity
and
recog-
nition
memory
in
healthy
young
adults.
We
used
a
paradigm
shown
in
fMRI
studies
to
recruit
the
hippocampus
[20,21]
in
com-
bination
with
serum
assays
for
resting
levels
of
BDNF;
related
neurotrophic
factors
insulin-like
growth
factor-1
(IGF-1),
and
vas-
cular
endothelial
growth
factor
(VEGF);
and
cortisol,
the
primary
glucocorticoid
hormone
in
humans.
We
predicted
an
interrelation-
ship
between
aerobic
capacity,
memory
performance,
and
hormone
levels.
Here
we
report
evidence
of
an
interaction
between
circulat-
ing
BDNF
and
aerobic
fitness
on
recognition
memory
performance
in
humans
and
show
how
other
trophic
factors
may
be
related
to
fitness.
2.
Materials
and
methods
2.1.
Participants
One
hundred
and
fourteen
healthy
young
participants
were
recruited
from
the
Boston
University
student
community.
Stu-
dents
who
indicated
their
interest
in
the
study
were
sent
a
brief
explanation
of
study
protocols,
and
asked
to
exclude
them-
selves
if
they
met
any
of
the
following
pre-screening
conditions:
history
of
neurological
or
psychiatric
conditions;
learning
dis-
ability;
heart,
lung,
or
musculoskeletal
conditions
or
disorders;
diabetes
mellitus;
electrolyte
disorder;
high
cholesterol;
eat-
ing
disorder
or
obesity;
or
current
use
of
any
prescription
or
recreational
cardioactive
or
psychoactive
drugs,
or
recreational
smoking.
Twenty-one
participants
were
excluded
following
an
initial
screening
visit,
twenty-one
withdrew
voluntarily,
and
an
addi-
tional
nine
participants
were
excluded
from
analyses
due
to
behavioral
tasks
performance
issues
(described
below;
N
=
2),
or
equipment
malfunction
(N
=
7),
leaving
a
final
sample
size
of
N
=
63
participants.
Details
of
participant
characteristics
are
presented
in
Table
1.
All
participants
were
native
English
speakers
or
bilingual,
all
had
normal
or
corrected
to
normal
vision,
and
all
gave
signed,
informed
consent
before
participating
in
the
experiment.
All
proto-
cols
were
approved
by
the
Boston
University
Charles
River
Campus
Institutional
Review
Board
and
conformed
with
the
Code
of
Ethics
of
the
World
Medical
Association
[22].
2.2.
Procedure
2.2.1.
Experimental
overview
For
each
participant,
the
experiment
consisted
of
three
visits:
(i)
informed
consent
and
screening,
(ii)
˙
VO
2
max
aerobic
capacity
and
body
composition
testing,
and
(iii)
blood
draw
and
cognitive
testing.
For
each
subject,
visits
took
place
approximately
within
one
month
from
start
to
finish,
and
visit
three
was
performed
no
later
than
one
week
after
visit
two.
2.2.2.
Consent
and
screening
Screening
ensured
participants
were
healthy
and
able
to
per-
form
a
strenuous
treadmill
test.
Participants
gave
signed,
informed
consent
during
the
first
visit
prior
to
the
start
of
any
other
procedures.
Participants
were
then
formally
screened
for
the
above
pre-screening
criteria.
In
addition,
height
and
weight
were
measured
to
calculate
body
mass
index
(BMI);
waist
and
hip
circumference
measurements
were
also
taken.
Participants
were
excluded
if
they
met
the
World
Health
Organization’s
criteria
for
obesity
on
both
of
these
measures:
BMI
greater
than
or
equal
to
30
for
both
men
and
women,
and
a
waist-to-hip
ratio
greater
than
or
equal
to
1.0
for
men,
or
0.8
for
women
(World
Health
Organi-
zation,
Geneva,
Switzerland).
Participants
also
completed
a
hand
preference
questionnaire
[23],
the
North
American
Adult
Reading
Test
[24,25]
to
estimate
verbal
intelligence
quotient,
and
the
Baecke
physical
activity
habits
questionnaire
[26].
At
the
end
of
the
con-
sent
and
screening
visit,
participants
were
given
an
exercise
and
physical
activity
diary
to
fill
out
over
the
next
two
weeks.
2.2.3.
Assessment
of
aerobic
capacity
To
assess
participants’
aerobic
capacity
(
˙
VO
2
max,
the
maximal
rate
of
oxygen
an
individual
consumes
during
exercise
measured
in
milliliters,
per
minute,
per
kilogram
body-mass)
we
used
a
graded
maximal
exercise
test
performed
on
a
treadmill
[27].
Briefly,
the
treadmill
started
out
at
a
speed
of
0.8
m/s,
and
an
incline
of
10%
grade.
Throughout
the
test,
the
speed
and
incline
of
the
treadmill
increased
by
an
average
of
0.35
m/s
and
2%
grade
every
three
min-
utes.
A
physician
was
on
call,
and
two
study
staff
members
with
current
cardio-pulmonary
resuscitation
certification
were
present
for
each
fitness
test.
All
˙
VO
2
max
tests
were
performed
between
the
hours
of
8:00
and
10:00
AM
at
Boston
University
Sargent
College
of
Health
and
Rehabilitation
Sciences.
The
ParvoMedics
True
One
2400
system
(ParvoMedics,
Sandy,
UT,
USA)
was
used
during
testing
to
analyze
gas
exchange
in
participants’
breath.
The
True
One
2400
system
was
calibrated
against
medical
grade
gasses
(ParvoMedics,
Sandy,
UT,
USA),
and
average
˙
VO
2
values
were
computed
over
30
s
intervals.
The
system
also
calculated
respiratory
exchange
ratio
(RER;
volume
expired
CO
2
divided
by
volume
expired
O
2
),
which
was
used
as
a
reference
value
for
calculated
˙
VO
2
max;
an
RER
around
or
above
1.15
is
indicative
˙
VO
2
max
has
been
reached
[28].
Participants
were
asked
not
to
engage
in
strenuous
physical
activity
for
24
h
before
testing,
and
not
to
consume
food
or
caffeine
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304
A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
Table
1
Participant
characteristics.
N
=
63
(39
female)
N
Range
Group
meansa
Correlationsb
Female
Male
accuracy
fitness
BDNF
cortisol
IGF-1
VEGF
Demographics
Age
(years)
63
18–29
20.5
±
2.1
22.3
±
3.2*
0.04
−0.23
0.09
0.07
−0.35**
0.03
Education
(years)
62
12–22
14.8
±
1.5
16.0
±
2.5*
−0.06
−0.24
0.06
0.11
−0.29*
−0.10
Physiology
BDNF
(ng
mL−1)
63
1.1–30.5
17.9
±
6.0
17.6
±
7.3
−0.23
−0.22
0.01
−0.12
0.15
Cortisol
(ng
mL−1)
62
14–182
72.8
±
31.9
60.1
±
35.5
−0.21
−0.25
0.01
−0.05
−0.10
IGF-1
(ng
mL−1)
62
95–271.4
165.1
±
42.6
159.0
±
30.1
0.11
0.27*
−0.12
−0.05
−0.08
VEGF
(ng
mL−1)
61
0.016–0.465
0.21
±
0.09
0.21
±
0.11
−0.05
−0.08
0.13
−0.06
−0.10
Fitness
percentile
63
3.3–99.9
51.4
±
24.0
70.2
±
26.3**
0.05
−0.22
−0.25
0.27*
−0.06
˙
VO
2
peak
(mL
kg−1min−1)
63
24.7–66.5
38.5
±
6.3
50.5
±
8.1***
0.02
0.91***
−0.17
−0.30**
0.18
−0.03
RER
max
63
0.98–1.56
1.19
±
0.14
1.26
±
0.15*
0.05
0.28*
0.03
−0.11
0.11
0.07
BMI
(kg
m
−2
)
63
16.8–31.2
22.9
±
3.2
23.6
±
3.2
−0.02
−0.18
−0.10
0.12
−0.03
−0.07
Body
fat
(%)
63
5.0–34.9
23.7
±
4.8
11.5
±
5.6***
0.03
−0.43***
0.11
0.36**
−0.09
0.08
Hip
circumference
(cm)
63
73.8–114.0
93.4
±
8.7
95.2
±
8.4
0.10
−0.23
−0.12
0.01
−0.05
0.06
Waist
circumference
(cm)
63
60.0–98.7
73.1
±
7.9
80.7
±
7.6***
−0.05
0.00
−0.11
0.02
−0.10
0.06
Questionnaires
Baecke
Work
index
62
1.25–3.625
2.1
±
0.4
2.1
±
0.6
0.00
0.14
−0.25*
0.13
−0.05
0.02
Baecke
Sport
index
62
1.0–4.5
2.5
±
0.8
3.0
±
0.8*
0.06
0.48***
−0.31*
−0.04
0.17
−0.33**
Baecke
Leisure
index
63
1.75–4.25
3.0
±
0.5
3.1
±
0.6
−0.03
0.20
−0.18
0.28*
0.05
−0.30*
Beck
depression
index
63
0–15
2.4
±
3.3
2.9
±
3.3
−0.17
0.17
−0.06
0.09
0.07
0.05
Estimated
verbal
IQ
62
98–123
112.4
±
5.3
112.8
±
4.8
0.07
−0.03
0.15
−0.01
−0.07
−0.07
Summarized
as
mean
±
SD;
asterisks
in
the
Male
column
indicate
sex
differences.
We
present
bivariate
Spearman-rank
coefficients
for
our
outcome
variables
to
provide
a
thorough
characterization
of
our
study’s
sample.
We
do
not
draw
inference
from
these
simple
comparisons.
“Accuracy”
refers
to
SMT
corrected
accuracy
(see
Section
2.2.7
for
definition).
P
<
0.05.
** P
<
0.01.
*** P
<
0.001.
3
h
prior.
Immediately
before
testing,
participants’
weight,
resting
heart
rate,
and
blood
pressure
were
measured,
and
skin-fold
calipers
were
used
to
estimate
percent
body
fat
using
the
three
site
formula
[29].
Participants
were
given
a
3–5
min
warm-up
at
0.5
m/s
before
testing.
Following
testing,
participants
were
given
a
five
minute
cool-down
at
0.5
m/s.
Participants’
blood
pressure,
heart
rate,
and
rating
of
perceived
exertion
[30]
were
monitored
continuously
during
testing
and/or
cool-down
in
accordance
with
American
College
of
Sports
Medicine
guidelines
(ACSM)
[29].
All
blood
pressure
measurements
were
taken
manually
with
an
inflatable
sphygmomanometer,
and
pulse
rates
were
measured
using
an
Omron
HR-100C
(Omron
Healthcare
Inc.,
Kyoto,
Japan)
heart
rate
monitor
or,
in
cases
of
monitor
failure,
were
measured
by
hand.
Fitness
tests
were
terminated
when
participants
reached
volitional
exhaustion.
Graded
exercise
protocol
and
termination
criteria
followed
ACSM
guidelines
[29].
2.2.4.
Blood
draw
All
participants
had
up
to
30
mL
of
blood
drawn
from
the
median
cubital
vein
by
a
trained
nurse
at
the
General
Clinical
Research
Unit,
Boston
University
Clinical
and
Translational
Science
Insti-
tute.
Blood
was
collected
to
analyze
serum
levels
of
brain
derived
neurotrophic
factor
(BDNF),
cortisol,
insulin-like
growth
factor-1
(IGF-1),
and
vascular
endothelial
growth
factor
(VEGF).
Because
BDNF
[31,32],
cortisol
(see
Ref.
[33]
for
a
review),
and
VEGF
[34]
fluctuate
with
circadian
rhythm,
all
venipunctures
were
performed
between
8:00
and
9:45
AM,
prior
to
cognitive
testing.
Though
not
explicitly
required,
participants
were
asked
to
fast
for
at
least
2
h
prior
to
the
blood
draw,
and
were
provided
a
small
breakfast
directly
after.
Whole
blood
was
drawn
to
a
serum
separator
tube
and
allowed
to
clot
at
room
temperature
for
approximately
30
min.
The
blood
samples
were
then
centrifuged
for
15
min
at
4C
and
1000
×
g.
One
mL
serum
aliquots
were
prepared
and
stored
at
−80
C.
Samples
were
stored
for
2–17
months
prior
to
analyses.
2.2.5.
Hormone
assays
Serum
BDNF,
cortisol,
IGF-1,
and
VEGF
were
determined
with
Quantikine
®
quantitative
sandwich
ELISA
kits
(R&D
Systems
Inc.,
Minneapolis,
MN,
USA).
All
assays
were
performed
according
to
the
manufacturer’s
instructions.
Assays
were
analyzed
in
two
rounds:
(i)
BDNF
and
cortisol
levels
were
determined
from
the
first
twenty-
eight
participants;
(ii)
IGF-1
and
VEGF
data
were
obtained
from
re-thawed
samples
from
the
first
twenty-eight
participants,
and
all
four
hormone
levels
were
determined
for
the
final
thirty-five
participants.
Within
each
round,
all
assays
were
performed
in
duplicate.
Assay
sensitivities,
or
minimum
detectable
concentrations,
for
BDNF,
cortisol,
IGF-1,
and
VEGF
assays
are
estimated
at
0.020,
0.071,
0.026,
and
0.005
ng
mL
−1
,
respectively.
Respective
intra-
and
inter-
assay
precision
coefficients
of
variation
for
BDNF,
cortisol,
IGF-1,
and
VEGF
are
estimated
at
less
than
6.2%,
9.2%,
4.3%,
and
6.5%
within-assay;
and
less
than
11.3%,
21.2%,
8.3%,
and
8.5%
between
assays.
No
significant
cross-reactivities
have
been
observed
for
BDNF
and
IGF-1
assays.
The
cortisol
assay
may
cross-react
with
the
synthetic
glucocorticoids
Prednisolone
(estimated
cross-reactivity:
4.4%)
and
Cortodoxone
(3.4%),
and/or
with
endogenous
steroid
hor-
mones
Progesterone
(1.7%)
and
Cortisone
(0.2%).
The
human
VEGF
assay
may
cross-react
with
human
VEGF
receptors
1
and
2
(genes
FLT1
and
KDR)
if
they
are
present
in
high
concentrations.
We
did
not
assay
for
any
of
these
cross-reactivity
factors.
2.2.6.
Cognitive
testing
We
used
an
adaptation
of
a
delayed
match-to-sample
(DMS)
paradigm
with
unfamiliar
complex
visual
scenes
in
combination
with
a
subsequent
memory
test
(SMT).
Thirty-three
of
the
current
study’s
participants
performed
the
DMS
phase
of
this
task
in
an
fMRI
scanner.
In
previous
fMRI
studies
we
have
demonstrated
this
paradigm
recruits
the
hippocampus
and
medial
temporal
lobe
cortex
[20,21].
All
stimulus
presentations
and
subject
response
data
were
displayed
and
recorded
using
EPrime
2.0
(Psychology
Software
Tools,
Pittsburgh,
PA),
and
all
cognitive
testing
was
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A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
305
Fig.
1.
Recognition
memory
task.
Adapted
from
Schon
et
al.
[20];
participants
were
first
shown
a
series
of
144
pseudo-randomized,
trial
unique
but
content
similar
outdoor
scenes
in
the
context
of
a
delayed
match
to
sample
(DMS)
working
memory
paradigm.
Approximately
15
min
later,
participants
were
administered
a
surprise
subsequent
memory
test
(SMT)
where
they
were
shown
all
144
DMS
images,
plus
144
lure
images,
and
asked
to
rate
their
recognition
confidence.
Participants
were
blind
to
the
ratio
of
old
to
new
images.
performed
in
the
morning,
approximately
within
60
min
after
the
blood
draw.
144
DMS
stimuli
were
drawn
from
a
set
of
288
trial
unique,
but
content
similar,
complex
visual
outdoor
scenes
(Fig.
1).
Participants
completed
eight
runs
of
twelve
trials
each.
For
each
trial,
participants
saw
a
sample
scene
(duration
=
2
s),
followed
by
a
delay
period
(duration
=
10
s),
a
test
scene
(duration
=
2
s),
and
an
inter-trial
interval
fixation
(duration
=
6
s;
or
6–14
s
jit-
tered
for
fMRI
participants).
Participants
were
asked
to
indicate
whether
sample
and
test
scenes
matched
or
not
using
keyboard
numbers
or
a
button-box
in
the
case
of
the
fMRI
participants,
and
to
respond
as
quickly
and
as
accurately
as
possible.
All
trials
were
evenly
split
between
match
and
non-match
conditions,
and
pseudo-randomized
for
each
run
and
participant.
Approximately
15
min
after
the
DMS
task,
participants
per-
formed
a
surprise
self-paced
SMT.
Participants
were
shown
all
144
old
images
randomized
with
an
equal
number
of
new
images
(lures).
Participants
were
unaware
of
the
ratio
of
old
to
new
images,
and
were
asked
to
rate
their
recognition
memory
strength
for
each
SMT
image
along
a
5-point
scale
[35]:
1-sure,
new;
2-unsure,
new;
3-unsure,
old;
4-sure,
old;
R-sure,
old,
plus
accompanied
by
some
subjective
association
with
the
scene,
e.g.
memory
for
which
run
the
scene
came
from
or
a
thought
prompted
by
the
original
viewing.
2.2.7.
Behavioral
performance
measures
For
the
DMS
task,
performance
was
assessed
using
simple
percent
correct
and
correct
trial
reaction
times.
Responses
from
incorrect
DMS
trials
were
discarded
from
SMT
analysis
on
a
per-
subject
basis.
For
the
subsequent
memory
test
(SMT),
since
an
image
could
either
be
old
or
new,
and
a
subject
could
classify
it
as
either
old
or
new,
all
responses
were
sorted
into
one
of
four
bins
on
a
standard
two
by
two
truth
table:
True
Positives
(hits)
and
Negatives
(correct
rejections),
and
False
Positives
(false
alarms)
and
Negatives
(misses).
Because
a
participant
could,
in
theory,
attain
a
perfect
True
Positive
rate
by
indiscriminately
classifying
all
stimuli
as
old,
we
used
the
corrected
hit
rate
(accuracy;
defined
as
hits
minus
false
alarms)
as
a
dependent
measure
of
recognition
memory
in
our
analyses.
Using
accuracy
instead
of
the
True
Positive
(hit)
rate
provides
a
partial
correction
for
this
potential
bias.
2.2.8.
Statistical
methods
All
raw
data
were
analyzed
in
R
2.15.2
[36].
Demographic
vari-
ables
are
summarized
as
mean
±
standard
deviation;
those
grouped
by
sex
were
analyzed
with
two-sample
t-tests
assuming
equal
vari-
ance
except
for
BMI,
which
is
typically
highly
skewed
[37],
and
fitness
percentile,
which
has
a
uniform
distribution.
These
excep-
tions
were
analyzed
with
the
Wilcoxon
rank
sum
test.
In
all
regression
analyses,
continuous
input
variables
were
cen-
tered
to
have
a
mean
of
zero,
except
fitness
percentile,
which
was
centered
to
the
50th
percentile
for
better
interpretability.
Centered
input
variables
were
then
standardized
by
dividing
by
twice
their
respective
marginal
standard
deviations
[38].
Resultant
regres-
sion
coefficients
can
be
interpreted
as
the
expected
change
in
y
per
mean
±
one
standard
deviation
in
x.
This
practice
allows
for
greater
consistency
when
comparing
coefficients
on
continuous
versus
binary
predictors
than
common
z-score
rescaling
[38].
Note
that
this
standardization
affects
the
scale,
but
not
the
shape
of
a
variable’s
distribution.
All
regression
coefficients
we
report
come
from
models
fit
to
these
standardized
inputs.
Binary
and
other
discrete
group
variables
were
left
un-centered
and
un-rescaled.
Throughout
the
modeling
process,
we
assessed
the
internal
valid-
ity
of
our
regression
models
by
comparing
estimated
coefficients
and
their
standard
errors,
adjusted-
or
pseudo-R
2
,
and
Akaike’s
information
criterion.
In
an
effort
to
control
for
inherent
sex
differences
in
aerobic
capacity
(men
typically
have
higher
˙
VO
2
max
due
to
larger
lung
capacity
and
lower
average
body
fat
percentages),
˙
VO
2
max
data
were
transformed
to
fitness
percentile
scores
calculated
based
on
norms
compiled
by
the
American
College
of
Sports
Medicine
(ACSM)
[29].
The
goal
of
this
transformation
was
to
put
observed
˙
VO
2
peak
values
on
an
age
and
gender
independent
scale.
Note
this
transformation
changes
the
shape
of
the
population
distribution
from
approximately
normal
(for
˙
VO
2
max),
to
uniform
(for
fitness
percentile;
Fig.
2).
Despite
this
effort
to
control
for
expected
sex
dif-
ferences,
we
included
sex
as
a
covariate
in
fitness
percentile-based
regression
analyses
as
we
observed
a
slight
gender
self-selection
bias:
in
our
data,
fit
individuals
with
high
˙
VO
2
max
scores
tended
to
be
male.
In
our
regression
analyses,
positive
values
on
the
gen-
der
coefficient
reflect
higher
outcomes
for
male
participants.
Since
exactly
half
of
our
participants
did
not
reach
the
1.15
RER
bench-
mark
(see
above)
and
so
may
not
have
reached
their
true
˙
VO
2
max,
we
created
an
indicator
variable
for
participants
with
RER
max
<
1.15
and
participants
with
RER
max
1.15.
This
group
variable
was
also
used
to
adjust
fitness
percentile-based
regression
analyses,
and
positive
values
on
its
coefficient
reflect
higher
outcomes
for
indi-
viduals
whose
RER
was
greater
than
or
equal
to
1.15.
Other
variables
of
interest
include
measures
of
memory
per-
formance,
and
hormone
concentrations
obtained
from
the
blood
sample
assays.
Since
all
memory
variables
are
expressed
as
pro-
portions
(e.g.
SMT
corrected
accuracy,
see
above),
they
make
for
inappropriate
outcome
variables
for
standard
ordinary
least
squares
(OLS)
regression
methods.
Proportions
are
often
skewed
and
heteroskedastic,
and
exist
only
on
the
closed
unit
interval
[0,
1].
We
have
therefore
modeled
our
memory
variables
using
the
beta
distribution,
which
is
more
appropriate
for
analysis
of
pro-
portions
[39,40].
This
was
accomplished
using
the
betareg
function
from
the
R
betareg
package
[41]
specifying
the
logit
link.
Resultant
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306
A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
Fig.
2.
Summary
of
raw
˙
VO
2
peak
data
and
fitness
percentile
transformation.
Box-
plots
and
raw
data
points
from
our
sample
are
overlaid
on
histograms
of
simulated
population
distributions
of
˙
VO
2
max
and
fitness
percentile
based
on
[29].
The
goal
of
transforming
˙
VO
2
peak
to
fitness
percentile
was
to
compare
individuals
on
an
age
and
gender
independent
scale.
Boxplots
indicate
quartiles
and
medians
for
˙
VO
2
peak
data
for
(top)
female
and
(middle)
male
participants,
and
fitness
percentile
data
for
both
genders
(bottom).
Individual
data
points
are
jittered
slightly
in
the
y
dimension
to
appear
more
visually
distinct.
regression
coefficients
can
then
be
exponentiated
and
interpreted
as
odds
ratios
(OR).
Prior
to
analysis,
since
two
participants
had
perfect
scores
and
the
beta
distribution
assumes
data
on
the
open
unit
interval
(0,
1),
DMS
accuracy
was
transformed
with
the
formula
[(x·(n
1)
½)/n]
where
n
is
the
number
of
observations
in
x
[40].
SMT
scores
were
left
untransformed
as
these
were
all
comfortably
within
the
(0,
1)
range.
For
the
reader’s
benefit,
we
present
both
OLS
and
beta
regression
results
side-by-side.
We
opted
to
log-transform
(using
the
natural
logarithm)
hor-
mone
level
measurements
whenever
they
were
used
as
outcome
variables.
Since
hormone
levels
are
all-positive
continuous
vari-
ables
they
are
likely
to
have
effects
on
a
multiplicative
scale,
making
the
log-transformation
an
appropriate
choice
for
improved
statis-
tical
inference
[42].
Since
serum
assays
were
conducted
in
two
rounds,
we
adjusted
hormone
regression
analyses
for
the
effect
of
assay
round.
Technical
reasons
for
this
are
twofold:
(i)
measure-
ments
are
expected
to
vary
slightly
between
assays
(see
above),
and
(ii)
round
one
samples
were
thawed
one
extra
time
prior
to
IGF-
1/VEGF
measurements,
which
may
have
partially
degraded
these
samples.
Positive
values
on
this
coefficient
reflect
higher
outcomes
for
the
second
assay
round.
Furthermore,
we
considered
hormone
by
fitness
interactions.
Given
the
results
of
our
analysis
of
subse-
quent
memory,
we
specified
a
full
model,
predicting
SMT
accuracy
from
BDNF,
fitness
percentile,
and
their
interaction,
controlling
for
group
effects
of
sex,
RER
1.15,
and
assay
round.
3.
Results
3.1.
Participants
Overall,
N
=
63
participants
completed
all
phases
of
the
study.
See
Table
1
for
an
overview
of
participant
characteristics,
including
summaries
of
demographic
and
physiological
variables,
and
ques-
tionnaire
scores.
One
subject
was
removed
from
analyses
of
serum
cortisol,
because
that
subject’s
measured
cortisol
concentration
exceeded
the
highest
standard
solution
concentration,
even
when
the
sample
was
reanalyzed.
This
subject’s
cortisol
levels,
therefore,
could
not
be
reliably
determined.
Insulin-like
growth
factor-1
(IGF-
1)
and
vascular
endothelial
growth
factor
(VEGF)
data
were
also
unavailable
for
one
subject.
This
subject
consented
to
BDNF
and
cortisol
but
not
IGF-1
and
VEGF
assays.
Moreover,
we
observed
one
clear
outlier
in
the
VEGF
data.
This
subject’s
measurement
was
more
than
four
standard
deviations
away
from
the
group
mean,
and
was
discarded
from
all
VEGF
analyses
and
summaries.
Raw
˙
VO
2
peak
scores
ranged
from
24.7
to
63.2
mL
kg
−1
min
−1
for
women,
and
38.7–66.5
mL
kg
−1
min
−1
for
men
in
our
sam-
ple;
corresponding
fitness
percentiles
ranged
from
the
3.3rd
to
99.9th
percentile
for
women,
and
the
22.8–99.9th
percentile
for
men
(Fig.
2).
Resulting
normative
fitness
percentiles
were
greater
for
men
in
our
sample
(W
=
280,
z
=
2.66,
P
=
0.008),
indicating
a
possible
gender
self-selection
bias.
While
we
have
good
coverage
of
the
expected
population
distributions
of
˙
VO
2
max
and
fitness
percentiles
for
women,
we
may
be
under-sampling
lower-fit
men
and/or
oversampling
higher-fit
men
(Fig.
2).
Roughly
one-third
of
our
participants
(N
=
22;
18
female)
did
not
attain
the
threshold
RER
of
1.15
and
so
may
not
have
reached
their
true
˙
VO
2
max.
3.2.
DMS
results
Accuracy
on
the
DMS
task,
assessed
as
a
simple
proportion
cor-
rect,
was
very
high
for
most
participants
(mean
=
95
±
4.6%),
and
was
not
predicted
by
fitness
in
either
beta
or
OLS
regression
mod-
els
(both
P
>
0.19).
When
the
models
were
adjusted
for
sex
and
RER
1.15,
however,
marginal
negative
relationships
between
fit-
ness
and
accuracy
emerged
(beta:
OR
=
0.74,
P
=
0.08,
95%
CI
=
[0.53,
1.03];
OLS:
ˇ
=
−0.02,
P
=
0.08,
95%
CI
ˇ
=
[−0.05,
0.002]).
Median
reaction
time
(in
milliseconds)
was
also
analyzed
for
correct
trials.
We
found
a
marginal
log-level
relationship
between
faster
reac-
tion
times
and
fitness
=
−0.11,
P
=
0.08,
95%
CI
ˇ
=
[−0.23,
0.01])
including
when
the
model
was
corrected
for
sex
and
RER
=
−0.11,
P
=
0.12,
95%
CI
ˇ
=
[−0.24,
0.02]).
We
did
not
expect
performance
on
the
DMS
task
to
be
associated
with
fitness.
These
data
seem
to
reflect
a
slight
speed-accuracy
tradeoff
which
may
be
incidentally
related
to
fitness,
and
we
do
not
interpret
them
further.
3.3.
SMT
results
3.3.1.
Relationship
between
hormone
levels
and
subsequent
recognition
memory
A
summary
of
SMT
results
and
a
brief
comparison
of
beta
and
OLS
regression
models
are
presented
in
Table
2;
see
Supplemen-
tary
Fig.
1
for
a
summary
of
raw
SMT
data.
Mean
SMT
corrected
accuracy
(see
Section
2.2.7;
mean
=
49
±
11%)
was,
expectedly,
sub-
stantially
lower
than
the
raw
True
Positive
rate
(mean
=
67
±
10%),
demonstrating
the
importance
of
the
accuracy
bias
correction.
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A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
307
Table
2
Regression
models
of
subsequent
memory
test
(SMT)
accuracy.
Relationship
between
hormone
levels
and
subsequent
recognition
memory
Predictor
Unadjusted
Adjusted
ˇa
R2
b
ˇa
ˇ
assay
round
c
R2
b
BDNF
−0.215
*
(0.109)
0.059
(0.045)
−0.200
(0.107)
−0.188
(0.106)
0.103
(0.034)
Cortisol
−0.145
(0.111)
0.026
(0.009)
−0.166
(0.107)
−0.243* (0.107)
0.100
(0.001)
IGF-1
0.063
(0.113)
0.005
(−0.011)
0.065
(0.110)
−0.213
(0.110)
0.062
(−0.026)
VEGF
−0.003
(0.115)
0.000
(−0.017)
0.048
(0.115)
−0.219
(0.115)
0.056
(0.025)
Relationship
between
aerobic
fitness
and
subsequent
recognition
memory
Predictor
Unadjusted
Adjusted
ˇ
fitness
a
R2b
ˇ
fitness
a
ˇ
sex
c
ˇ
RER≥1.15
R2b
Fitness
−0.002
(0.112)
0.000
(−0.016)
0.008
(0.118)
−0.086
(0.125)
0.128
(0.121)
0.020
(−0.029)
Interaction
between
BDNF
and
aerobic
fitness
on
subsequent
recognition
memory
ˇ
BDNF·fitness
ˇ
BDNF
a
ˇ
fitness
a
ˇ
sex
c
ˇ
RER≥1.15
ˇ
assay
round
c
R2b
0.609** (0.228)
−0.294** (0.109)
−0.059
(0.105)
0.157
(0.117)
0.587** (0.222)
−0.252* (0.106)
0.040
(0.118)
−0.066
(0.116)
0.181
(0.113)
−0.262* (0.113)
0.232
(0.158)
This
table
presents
a
series
of
beta
regression
models
with
and
without
adjusting
for
confounding
covariates;
each
row
corresponds
to
a
different
model
predicting
SMT
accuracy.
Coefficients
are
presented
as
ˇ
(SE
ˇ
).
Exponentiated
coefficients
can
be
interpreted
as
odds-ratios.
a
Continuous
input
variables
were
standardized
prior
to
model
estimation
(see
Section
2
for
details).
Beta
regression
pseudo-R2 (ordinary
least
squares
regression
adjusted-R2 for
comparison).
Positive
values
on
the
coefficient
for
assay
round
reflect
higher
outcomes
for
assay
wave
two;
positive
values
on
the
coefficient
for
sex
reflect
higher
outcomes
for
male
subjects.
P
<
0.05.
** P
<
0.01.
The
two
measures
were,
however,
predictably
moderately
corre-
lated
(Spearman’s
=
0.56,
P
<
0.001).
In
unadjusted
beta
regression
models,
BDNF
was
a
significant
negative
predictor
of
SMT
accu-
racy
(Fig.
3;
OR
=
0.81,
P
=
0.05,
95%
CI
=
[0.65,
0.999]);
cortisol,
Fig.
3.
Summary
of
BDNF
and
IGF-1
findings.
Top:
maximum
likelihood
beta
regression
models
of
subsequent
memory
accuracy
by
resting
serum
brain-derived
neurotrophic
factor
(BDNF)
and
insulin-like
growth
factor-1
(IGF-1).
Corrected
accu-
racy
is
defined
as
the
proportion
of
subsequent
memory
test
(SMT)
stimuli
correctly
identified
as
old
less
the
proportion
of
SMT
lure
stimuli
incorrectly
identified
as
old.
The
top-left
panel
shows
a
significant
negative
association
between
BDNF
and
SMT
accuracy.
Bottom:
ordinary
least
squares
(OLS)
regression
models
of
BDNF
and
IGF-1
by
cardiovascular
fitness
percentile.
Outcome
variables
were
modeled
on
the
log
scale.
The
bottom-right
panel
shows
strong
evidence
of
a
positive
association
between
IGF-1
and
fitness.
Aggregated
gray
lines
represent
95%
confidence
interval
estimates
for
each
regression.
IGF-1,
and
VEGF
were
all
non-significant
(all
P
>
0.19).
Results
were
comparable
when
models
were
adjusted
for
assay
round,
except
cortisol
became
a
marginal
negative
predictor
of
accuracy
(OR
=
0.85,
P
=
0.12,
95%
CI
=
[0.69,
1.04]),
and
the
effect
of
BDNF
on
SMT
accuracy
weakened
very
slightly
(OR
=
0.82,
P
=
0.06,
95%
CI
=
[0.66,
1.01]).
Assay
round
itself
was
at
least
marginally
signif-
icant
in
all
cases
(all
P
<
0.08).
Together,
these
results
point
to
a
negative
relationship
between
resting
BDNF
and
subsequent
recog-
nition
memory.
Supplementary
material
related
to
this
article
can
be
found,
in
the
online
version,
at
http://dx.doi.org/10.1016/j.bbr.2013.11.023.
3.3.2.
Relationship
between
aerobic
fitness
and
subsequent
recognition
memory
A
summary
of
SMT
results
and
a
brief
comparison
of
beta
and
OLS
regression
models
are
presented
in
Table
2.
Fitness
percentile
did
not
predict
SMT
accuracy
in
unadjusted
beta
regression
models.
Results
did
not
change
appreciably
when
models
were
adjusted
for
sex
and
RER
the
1.15
benchmark.
3.3.3.
Interaction
between
BDNF
and
aerobic
fitness
on
subsequent
recognition
memory
A
summary
of
SMT
results
and
a
brief
comparison
of
beta
and
OLS
regression
models
are
presented
in
Table
2.
We
also
considered
possible
interactions
between
hormones
and
fitness
on
subsequent
recognition
memory.
Given
the
results
of
the
above
SMT
anal-
ysis,
we
modeled
accuracy
from
predictors
BDNF,
fitness,
and
a
BDNF
by
fitness
interaction,
controlling
for
confounding
variables
sex,
RER
1.15,
and
assay
round.
Results
of
this
analysis
(Fig.
4)
indicated
main
effects
of
BDNF
(OR
=
0.78,
P
=
0.02,
95%
CI
=
[0.63,
0.96])
and
assay
round
(OR
=
0.77,
P
=
0.02,
95%
CI
=
[0.62,
0.96]),
and
a
strong,
positive
interaction
between
BDNF
and
fitness
predict-
ing
SMT
accuracy
(OR
=
1.80,
P
=
0.01,
95%
CI
=
[1.16,
2.78];
overall
model:
R
2
pseudo
=
0.23).
Note
the
interaction
term
coefficient
and
standard
error
remained
relatively
stable
when
sex
was
included
as
a
covariate
in
the
model
(Table
2).
Although
the
coefficient
on
resting
BDNF
suggests
a
negative
relationship
with
subsequent
308
A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
Fig.
4.
Interaction
between
BDNF
and
fitness
on
subsequent
memory
accuracy.
SMT
corrected
accuracy
was
modeled,
using
beta
regression,
as
a
function
of
resting
serum
BDNF,
cardiovascular
fitness
percentile,
and
their
interaction,
adjusting
for
confounding
covariates
sex,
an
indicator
of
aerobic
threshold
(RER
1.15),
and
assay
round.
The
top
panel
conveys
the
shape
of
a
strong,
positive
interaction
between
continuous
variables
BDNF
and
fitness
percentile.
For
example,
at
low
fitness,
increasing
serum
BDNF
clearly
predicts
lower
SMT
accuracy.
As
fitness
increases,
BDNF
begins
to
positively
predict
subsequent
memory
accuracy,
with
an
estimated
inflection
point
around
the
75th
aerobic
fitness
percentile.
Bottom
panels
show
effects
of
covariates
on
the
regression
at
mean
BDNF
and
50th
fitness
percentile.
recognition
accuracy,
this
relationship
becomes
positive
as
fitness
percentile
increases
(Fig.
4),
with
an
inflection
point
estimated
around
the
75th
fitness
percentile.
Given
we
also
observed
a
possible
self-selection
bias
of
higher-
fit
male
participants
we
wanted
to
confirm
the
BDNF
by
fitness
interaction
could
not
simply
be
explained
by
a
BDNF
by
sex
interaction.
Adding
this
second
interaction
term
to
the
model
strengthened
the
BDNF
by
fitness
interaction
(beta
regression
ˇ
accuracy
±
SE
ˇ
increased
from
0.59
±
0.22
to
0.70
±
0.24),
indicating
the
observed
BDNF
by
fitness
term
should
not
be
taken
as
simply
a
substitute
for
a
BDNF
by
sex
term.
The
BDNF
by
sex
coefficient
was
not
significant
(P
=
0.23).
Furthermore,
adding
the
BDNF
by
sex
term
to
the
model
had
a
minimal
effect
on
R
2
pseudo
estimates
(R
2
pseudo
increased
by
0.017)
and
slightly
lessened
the
model’s
expected
out
of
sample
predictive
power,
given
by
a
small
increase
in
Akaike’s
information
criterion.
For
these
reasons
we
discarded
the
two-
interaction
term
model
in
favor
of
the
simpler
model
presented
above,
and
detailed
in
Table
2
and
Fig.
4.
3.4.
Hormone
measures
3.4.1.
Relationship
between
hormone
levels
and
aerobic
capacity
Unadjusted
ordinary
least
squares
(OLS)
regression
models
showed
a
marginal
negative
log-level
association
between
corti-
sol
and
fitness
=
−0.20,
P
=
0.09,
95%
CI
ˇ
=
[−0.43,
0.03]),
and
a
significant
positive
log-level
relationship
between
IGF-1
and
fit-
ness
(Fig.
3;
ˇ
=
0.12,
P
=
0.02,
95%
CI
ˇ
=
[0.02,
0.22]).
Associations
between
BDNF
and
VEGF
and
fitness
were
non-significant
(both
P
>
0.34).
When
the
models
were
adjusted
for
sex,
RER
1.15,
and
assay
round,
IGF-1
remained
positively
associated
with
fitness
=
0.16,
P
=
0.008,
95%
CI
ˇ
=
[0.05,
0.28]).
Fitness
did
not
signif-
icantly
predict
log-hormone
levels
in
the
other
models
(BDNF:
ˇ
=
−0.17,
P
=
0.22,
95%
CI
ˇ
=
[−0.45,
0.10];
Cortisol:
ˇ
=
−0.13,
P
=
0.35,
95%
CI
ˇ
=
[−0.39,
0.14];
VEGF:
ˇ
=
−0.20,
P
=
0.16,
95%
CI
ˇ
=
[−0.47,
0.08]).
3.4.2.
Hormone-sex,
hormone-hormone,
and
hormone-assay
round
comparisons
A
summary
of
hormone
concentration
ranges
and
means
by
sex
is
presented
in
Table
1.
Of
BDNF,
cortisol,
IGF-1,
and
VEGF,
only
cortisol
showed
marginal
evidence
of
sex
differences.
We
found
evidence
of
a
log-log
relationship
between
BDNF
and
VEGF
=
0.48,
P
<
0.001,
95%
CI
ˇ
=
[0.26,
0.71]),
including
with
the
cor-
rection
for
assay
round
BDNF
=
0.46,
P
<
0.001,
95%
CI
ˇ
=
[0.24,
0.68]);
all
other
hormone
correlation
pairs
were
non-significant
(all
P
0.20).
Only
VEGF
showed
evidence
of
group
differences
between
assay
rounds.
Mean
VEGF
measurement
was
lower
for
assay
round
one,
where
samples
were
thawed
one
extra
time
for
VEGF/IGF-
1
analysis
(mean
=
0.186
±
0.094,
round
1;
0.230
±
0.092
ng
mL
−1
,
round
2;
t
(59)
=
1.99,
P
=
0.05;
all
other
P
>
0.38).
This
suggests
the
additional
thaw
cycle
may
have
degraded
VEGF
levels
to
some
degree,
although
we
should
note
our
observed
VEGF
range
was
comparable
to
ranges
reported
in
previous
studies
(e.g.
[43,44]).
4.
Discussion
4.1.
Summary
of
results
in
the
context
of
animal
models
and
human
studies
The
current
study
was
designed
to
probe
relationships
between
cardio-respiratory
fitness,
brain-derived
neurotrophic
factor
(BDNF),
cortisol,
insulin-like
growth
factor-1
(IGF-1),
vas-
cular
endothelial
growth
factor
(VEGF),
and
hippocampal
memory
in
healthy
young
adults.
We
measured
aerobic
capacity
(
˙
VO
2
peak);
resting
levels
of
serum
BDNF,
cortisol,
IGF-1,
and
VEGF;
and
assessed
recognition
memory.
Cotman
et
al.
[3]
introduced
a
framework
where
aerobic
exercise
induces
upregulated
expres-
sion
of
growth
factors
throughout
the
body,
importantly
IGF-1
and
VEGF.
Circulating
IGF-1
and
VEGF
can
then
either
cross
or
act
on
the
blood-brain
barrier
and
modulate
expression
of
brain
IGF-1,
VEGF,
and
BDNF
which
in
turn
effect
increased
brain
plasticity
mecha-
nisms
that
may
benefit
cognitive
function
[3].
In
support
of
this
framework,
on
a
basic
level,
we
have
shown
resting
serum
IGF-1
is
positively
associated
with
aerobic
fitness,
and
BDNF
and
VEGF
lev-
els
are
also
correlated
with
one
another.
At
a
more
complex
level,
our
data
suggest
BDNF
and
fitness
interact
to
predict
recognition
memory
accuracy.
At
present
human
and
non-human
animal
studies
of
the
effects
of
exercise
and
aerobic
capacity
on
cognitive
function
are
somewhat
disconnected.
It
is
well
established
aerobic
exer-
cise
and
environmental
enrichment
induce
beneficial
effects
on
the
hippocampus
and
hippocampal
memory
system
in
rodents
[1,2,45–49].
In
contrast,
human
studies
have
mostly
investigated
the
effects
of
exercise
on
broad
cognitive
domains,
such
as
exec-
utive
functions,
and
until
recently
have
not
targeted
hypotheses
toward
specific
brain
regions
(reviewed
in
Ref.
[10]).
In
terms
of
putative
hippocampal
memory
tasks,
a
study
by
Pereira
et
al.
[16]
showed
delayed
free
recall
performance
was
positively
cor-
related
with
fitness
in
a
sample
of
young
to
middle-aged
adults.
In
addition,
relational
memory
performance,
has
been
associated
with
aerobic
capacity
in
pre-adolescent
children
[50–52].
Finally,
change
in
˙
VO
2
peak
has
recently
been
shown
to
modulate
brain
activity,
measured
with
fMRI,
associated
with
task
performance
A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
309
on
a
virtual
navigation
task
[53],
a
paradigm
shown
to
recruit
the
hippocampus
[54,55].
None
of
these
studies
have
reported
relation-
ships
between
BDNF
and
memory.
Complementary
to
these
studies,
we
have
assessed
the
relationship
between
BDNF,
memory,
and
physical
fitness
in
healthy
young
adults
using
a
memory
paradigm
shown
to
recruit
the
hippocampus.
With
this
study,
we
are
among
the
first
few
groups
to
address
this
gap
in
the
literature.
We
selected
our
recognition
memory
task
because
it
has
been
shown
to
engage
the
hippocampus
in
fMRI
studies
[20,21],
and
depend
on
hippocam-
pal
integrity
in
patient
studies
[56],
and
so
should
provide
a
human
parallel
to
the
memory
tasks
used
in
rodent
studies.
4.2.
Aerobic
fitness
as
a
modifier
of
the
relationship
between
BDNF
and
recognition
memory
We
have
observed
a
large
interaction
effect
suggesting
the
rela-
tionship
between
BDNF
and
memory
may
be
modulated
by
aerobic
fitness.
Since
BDNF
mRNA
and
expression
is
increased
in
the
hip-
pocampus
in
response
to
aerobic
exercise
[7,8,57],
the
protein
has
long
been
considered
a
candidate
component
of
the
physiologi-
cal
mechanisms
underlying
the
effects
of
aerobic
exercise
on
the
hippocampal
memory
system.
Animal
models
have
suggested
exer-
cise
benefits
memory
function
specifically
by
modulating
BDNF
related
mechanisms
[58–63].
In
support
of
these
hypotheses,
our
results
suggest
BDNF
and
aerobic
fitness
may
interact
to
predict
recognition
memory
in
humans.
Although
aerobic
capacity
per
se
is
not
typically
measured
in
rodent
models,
it
is
well
established
that
exercise
training
increases
aerobic
capacity
dependent
on
frequency,
duration,
and
intensity
of
training
[64–66].
This
sug-
gests
aerobic
fitness
may
underlie
the
benefits
of
exercise
on
brain
health
and
cognition
observed
in
animal
models.
Consistent
with
this
hypothesis,
previous
work
in
older
adults
has
shown
aero-
bic
capacity
may
correlate
with
overall
hippocampal
volume,
both
cross-sectionally
and
following
a
yearlong
aerobic
exercise
inter-
vention
[18,67].
In
our
study
an
interaction
between
serum
BDNF
and
aerobic
fitness,
but
not
fitness
alone,
predicted
recognition
memory.
It
is
always
challenging
to
interpret
interactions
between
continuous
variables.
It
can
be
even
more
difficult
in
this
case
since
we
did
not
observe
a
main
effect
of
fitness
to
accompany
the
BDNF
by
fitness
interaction—however,
in
our
view
this
is
a
positive
out-
come:
we
would
not
want
a
single
variable
as
general
as
“fitness”
to
explain
a
considerable
amount
of
variance
in
memory
performance
in
healthy
university
students.
Aerobic
fitness
is
only
one
of
many
indices
of
healthy
physiological
function.
We
would
expect
fitness
to
interact
with
many
other
physiological
parameters,
and
our
data
imply
fitness
should
be
interpreted
in
the
context
of
these
vari-
ables.
Our
results
indicate
a
strong,
positive
interaction
between
BDNF
and
fitness
such
that
resting
serum
BDNF
is
negatively
pre-
dictive
of
mean
recognition
memory
accuracy
at
low
fitness,
and
positively
predictive
at
high
fitness.
Although
we
observed
a
poten-
tial
gender
self-selection
bias
which
likely
adds
some
noise
to
our
estimated
effects,
we
argue
the
BDNF-fitness
interaction
cannot
be
wholly
attributable
to
gender
differences.
Indeed,
our
analysis
suggests
sex
plays
a
relatively
small
role
in
the
other
effects
we
report
on.
The
coefficients
BDNF
and
the
BDNF-fitness
interaction
remained
stable
when
sex
was
included
in
our
full
model
of
SMT
performance,
and
the
coefficient
on
sex
added
very
little
informa-
tion
to
the
models
overall
(Table
2;
also
see
Section
3.3.3).
While
there
are
likely
alternative
interpretations
for
the
interaction
effect,
one
possible
explanation
that
fits
well
with
the
extant
literature
is
that
there
may
be
an
exercise
adaptation
related
change
in
the
BDNF
dose–response
function.
Exercise
is
thought
to
act
on
BDNF
and
its
signaling
pathways
through
a
variety
of
mechanisms.
It
is
well
established
exercise
increases
hippocampal
mRNA
and
protein
levels
of
BDNF
[7,8,57]
and
its
“high”
affinity
tyrosine
kinase
receptor,
tropomyosin
recep-
tor
kinase
B
[68,69].
Exercise
may
also
act
to
increase
the
density
of
BDNF
in
dendrites
[70],
and
the
activity
of
tissue-type
plasmino-
gen
activator
[62]—a
protein
related
to
the
proteolytic
conversion
of
BDNF
into
its
mature
form.
BDNF
is
synthesized
as
a
pro-
form,
which
can
trigger
apoptotic
pathways
via
the
low
affin-
ity
p75
neurotrophin
receptor.
Classically
“beneficial”
effects
of
BDNF
signaling
are
thought
to
be
greatest
when
the
balance
of
neurotrophin/receptor
binding
shifts
toward
mature
BDNF
and
tropomyosin
receptor
kinase
B
(see
Ref.
[71]
for
a
review).
BDNF,
IGF-1,
and
other
neurotrophic
factors,
moreover,
are
thought
to
be
important
agents
of
healthy
cellular
function
and
mechanisms
of
hormesis,
including
in
neurons
[72–74].
It
is
hypothesized
cells
adapt
to
free
radical
induced
accumulation
of
toxins
and
oxida-
tive
stress
through
a
collection
of
hormetic
mechanisms
whereby
low
doses
of
these
stressors
or
toxins
induce
a
positive
effect
on
cells
and
cell
systems
(see
Ref.
[75]
for
a
review).
Exercise
adapta-
tion
and
conditioning
is
thought
to
mirror
hormesis
by
generating
oxidative
damage
the
body
must
then
work
to
repair,
improving
systemic
capacity
to
deal
with
reactive
oxygen
species
in
the
future
(see
Ref.
[76]
for
a
review).
This
literature
suggests
BDNF
and
IGF-
1
may
be
differentially
regulated
in
individuals
whose
physiology
has
adapted
to
the
metabolic
demands
of
chronic
exercise,
and
our
results
may
be
taken
as
a
compelling
behavioral
indicator
of
this
putative
effect.
A
possible
alternative
is
that
BDNF
may
moderate
an
aerobic
fitness
dose–response
curve.
We
are
not
able
to
distinguish
these
interpretations
with
the
present
study
design.
Given
the
ubiquity
of
multiple
feedback
mechanism
in
biological
systems,
it
is
likely
the
signaling
mechanisms
are
bidirectional
to
some
degree.
We
do,
however,
give
slight
preference
to
the
BDNF
dose–response
inter-
pretation
because
we
observed
a
main
effect
of
BDNF
but
not
fitness
in
our
fully
specified
model
of
SMT
accuracy
(Table
2).
It
will
be
par-
ticularly
insightful
to
probe
causal
and
directional
components
of
this
interaction
in
future
studies.
4.3.
Peripheral
versus
central
BDNF
and
hippocampal
memory
function
Researchers
studying
the
effects
of
BDNF
on
brain
plasticity
in
animal
and
cellular
models
are
able
to
measure
the
protein
directly
from
nervous
tissue.
Although
BDNF
cannot
be
measured
directly
in
the
living
human
brain,
various
reports
indicate
periph-
eral
BDNF
may
be
a
viable
surrogate
for
BDNF
measured
centrally.
For
example,
serum
BDNF
concentrations
are
correlated
with
over-
all
brain
BDNF
concentrations
in
multiple
species
[77],
and,
in
a
mouse
model,
hippocampal
BDNF
was
increased
after
peripheral
BDNF
infusion
[78].
BDNF,
cortisol,
and
IGF-1
can
all
cross
the
blood-brain
barrier
[79–82],
and
although
VEGF
does
not
cross
the
blood-brain
barrier,
peripheral
VEGF
may
induce
brain
plasticity
effects
through
a
variety
of
other
mechanisms
(see
Ref.
[83]
for
a
review).
Together,
these
studies
suggest
our
serum
measurements
may
be
a
reasonable
approximation
for
levels
of
these
hormones
in
brain
and
hippocampus.
While
other
human
studies
have
con-
sidered
serum
BDNF
in
an
aerobic
exercise
and
learning/memory
paradigm
[18,19,84,85],
none
have
reported
relationships
between
BDNF
and
memory.
Our
results
extend
previous
work
by
suggest-
ing
(i)
a
negative
association
between
resting
serum
BDNF
and
recognition
memory
accuracy
and
(ii)
that
this
relationship
may
be
moderated
by
aerobic
fitness.
These
findings
are
particularly
exciting
for
the
field
because
they
are
the
first
demonstration
of
a
relationship
between
BDNF
and
memory
in
a
sample
of
healthy
adults.
Related
work
has
found
serum
and
hippocampal
BDNF
are
decreased
in
various
pathological
states,
for
example
in
Alzheimer’s
disease
and
major
depression
[86–92].
Another
study
found
plasma
310
A.S.
Whiteman
et
al.
/
Behavioural
Brain
Research
259 (2014) 302–
312
BDNF
was
positively
correlated
with
performance
on
various
neu-
ropsychological
tests
in
older
women
[93].
Given
this
background,
and
our
results,
it
seems
clear
different
dose–response
mecha-
nisms
may
be
at
play
between
healthy
and
patient
populations.
We
speculate
that
in
patients
and
the
elderly,
peripheral
BDNF
might
co-vary
most
with
overall
health,
while
it
might
be
more
tightly
coupled
to
complex
physiological
factors
in
healthy
young
adults
as
in
our
sample.
Peripheral
BDNF
has
been
linked
to
a
vari-
ety
of
homeostatic
mechanisms,
including
blood
glucose
control
and
energy
metabolism
[94,95],
angiogenesis
and
vascular
sta-
bility
[96],
inflammation
and
pain
transduction
(reviewed
in
Ref.
[97]),
etc.
As
far
as
is
known,
serum
BDNF,
which
we
measured
directly,
is
derived
mainly
from
immune
system
related
periph-
eral
blood
mononuclear
cells,
platelets,
and
vascular
endothelial
cells
[87,98–101].
This
highlights
the
need
for
studies
like
ours
to
characterize
these
physiological
and
behavioral
relationships
in
healthy
individuals,
where
latent
age-related
confounds
do
not
complicate
interpretation
of
results.
A
thorough
understanding
of
healthy
physiology
is
a
necessary
prerequisite
for
an
understanding
of
pathophysiology.
Since
peripheral
BDNF
is
correlated
with
brain
and
hippocampal
BDNF
as
suggested
by
animal
work
[77],
it
may
be
physiologi-
cally
relevant
to
episodic
memory
function.
Consequently,
evidence
that
relates
aerobic
exercise
to
BDNF
and
hippocampal
memory
in
rodents
might
be
taken
to
suggest
our
serum
BDNF
measurements
should
be
positively
associated
with
recognition
memory.
We
dis-
cuss
this
point
from
two
angles.
First,
evidence
suggests
levels
of
BDNF
protein
in
rodent
hippocampus
is
significantly
increased
from
baseline
only
after
about
two
to
three
weeks
of
consistent
wheel
running
[63].
This
implies
extant
rodent
models
may
be
most
directly
comparable
to
our
higher-fit
participants.
Consistent
with
this
hypothesis,
taking
our
point-estimates
for
the
coefficients
on
serum
BDNF
and
the
BDNF
by
fitness
interaction
at
face
value,
our
data
suggest
BDNF
positively
predicts
mean
recognition
memory
accuracy
only
in
individuals
above
the
75th
aerobic
fitness
per-
centile,
although
the
error
of
this
estimate
is
quite
large.
In
this
age
range,
the
75th
percentile
corresponds
to
˙
VO
2
max
scores
of
about
43
mL
kg
−1
min
−1
for
women,
and
49
mL
kg
−1
min
−1
for
men.
Despite
these
observations
in
humans
it
is
unknown
if
endogenous
peripheral
BDNF
levels
are
associated
with
hippocampal
memory
in
animal
models.
Second,
inference
drawn
from
comparisons
of
hormone
levels
needs
to
be
placed
in
its
specific
context.
For
example,
the
tim-
ing
between
blood
sample
collection
for
BDNF
assays
and
exercise
is
critical,
because
circulating
BDNF
levels
exhibit
regular
diurnal
fluctuation
patterns
[31,32],
and
peak
in
response
to
acute
exercise
(reviewed
in
Ref.
[102]).
We
have
shown
resting
serum
BDNF,
mea-
sured
from
blood
drawn
in
the
morning,
shortly
before
cognitive
testing,
is
negatively
associated
with
recognition
memory
perfor-
mance
in
healthy
young
adults.
Since
BDNF
is
a
dynamic
variable,
there
may
be
additional
experimental
manipulations
that
might
reverse
this
outcome.
Future
animal
research
is
needed
that
more
closely
resembles
the
human
work
by
(i)
measuring
or
estimat-
ing
aerobic
fitness
(which
is
possible
in
rodents
[103]),
and
(ii)
by
including
BDNF
measurements
from
peripheral
blood
samples.
4.4.
Relationship
between
resting
BDNF
and
IGF-1
and
aerobic
fitness
Here
we
have
given
evidence
resting
serum
IGF-1
is
positively
associated
with
aerobic
fitness
in
healthy
young
adults.
This
is
in
accord
with
extant
literature
in
older
adults
that
suggests
a
posi-
tive
relationship
between
aerobic
capacity
and
IGF-1
[104,105].
We
have
also
given
an
estimate
for
the
relationship
between
BDNF
and
fitness.
Currie
et
al.
[106]
described
a
negative
relationship
between
resting
serum
BDNF
and
estimated
˙
VO
2
max
in
young
to
middle
aged
adults
that
is
consistent
with
our
estimated
negative
rela-
tionship.
When
we
replicated
Currie
and
colleagues’
analysis
of
the
relationship
between
serum
BDNF
and
˙
VO
2
peak
with
our
data
we
found
the
95%
confidence
intervals
for
each
data
set’s
Pearson-r
statistics
include
the
other’s
estimate
(unpublished
observation;
we
refer
interested
readers
to
our
similar
comparison
between
BDNF
and
aerobic
fitness
percentile,
reported
here).
Although
the
correlation
is
not
statistically
significant
in
our
data,
this
observa-
tion
suggests
the
underlying
population
parameter
may
be
well
approximated
in
both
studies.
Together
with
other
studies
that
report
an
inverse
relationship
between
serum
BDNF
and
fitness
measures
[107,108],
these
findings
suggest
resting
levels
of
circu-
lating
BDNF
are
on
average
slightly
lower
in
higher-fit
individuals.
4.5.
Other
interpretive
considerations
Although
we
have
made
an
effort
to
address
potential
sources
of
confounding
influence
in
our
data
collection
and
analysis,
we
acknowledge
other
potential
limitations
to
our
results.
For
exam-
ple,
we
have
observed
a
possible
gender
self-selection
bias
whereby
we
may
have
undersampled
lower-fit
males
and/or
oversampled
higher-fit
males.
We
have
endeavored
to
control
for
this
by
convert-
ing
raw
˙
VO
2
peak
scores
to
ACSM
fitness
percentiles
and
adjusting
for
effects
of
sex
in
our
regression
analyses.
Despite
these
precau-
tions,
however,
we
may
not
have
completely
eliminated
this
bias
in
our
results.
Another
potential
limitation
to
the
present
report
is
its
dropout
rate.
It
is
unclear
how
our
low
study
completion
rate
(55%)
may
have
influenced
our
results.
Perhaps
most
importantly,
we
note
the
magnitudes,
causal
directions,
and
replicability
of
the
effects
we
report
here
will
need
to
be
assessed
in
future
studies.
4.6.
Conclusions
Results
of
this
study
provide
evidence
for
a
novel
negative
association
between
resting
serum
BDNF
and
recognition
mem-
ory
accuracy